class_docstr: A ready-to-use Response Generator  model
  
  The class can be used to generate and continue dialogue given the user input.
  The given YAML must contain the fields specified in the *_NEEDED[] lists.
  It needs to be used with custom.py to load the expanded Llama2 model with added tokens like bos,eos, and speaker's tokens.
  
  Example
  -------
  >>> from speechbrain.inference.text import Llama2ResponseGenerator
  
  >>> tmpdir = getfixture("tmpdir")
  >>> res_gen_model = Llama2ResponseGenerator.from_hparams(source="speechbrain/MultiWOZ-Llama2-Response_Generation",
  ... savedir="tmpdir",
  ... pymodule_file="custom.py")  # doctest: +SKIP
  >>> response = res_gen_model.generate_response("I want to book a table for dinner")  # doctest: +SKIP
code: def __init__(self, *args, **kwargs):
      run_opts = {"device": "cuda"}
      super().__init__(run_opts=run_opts, *args, **kwargs)
  
imports: import torch
  from itertools import chain
  from speechbrain.inference.interfaces import Pretrained, EncodeDecodePipelineMixin
label_desc: Use image node_id 1 to create a new Llama2ResponseGenerator object from inherited base classes: ResponseGenerator with example: obj = Llama2ResponseGenerator()